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        <title>Translating Headers of Tabular Data: A Pilot Study of Schema Translation</title>
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    <abstract>Schema translation is the task of automatically translating headers of tabular data from one language to another. High-quality schema translation plays an important role in cross-lingual table searching, understanding and analysis. Despite its importance, schema translation is not well studied in the community, and state-of-the-art neural machine translation models cannot work well on this task because of two intrinsic differences between plain text and tabular data: morphological difference and context difference. To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese. Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context. Specifically, we model a target header and its context as a directed graph to represent their entity types and relations. Then CAST encodes the graph with a relational-aware transformer and uses another transformer to decode the header in the target language. Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models. Our dataset will be released at https://github.com/microsoft/ContextualSP.</abstract>
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%0 Conference Proceedings
%T Translating Headers of Tabular Data: A Pilot Study of Schema Translation
%A Zhu, Kunrui
%A Gao, Yan
%A Guo, Jiaqi
%A Lou, Jian-Guang
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zhu-etal-2021-translating
%X Schema translation is the task of automatically translating headers of tabular data from one language to another. High-quality schema translation plays an important role in cross-lingual table searching, understanding and analysis. Despite its importance, schema translation is not well studied in the community, and state-of-the-art neural machine translation models cannot work well on this task because of two intrinsic differences between plain text and tabular data: morphological difference and context difference. To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese. Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context. Specifically, we model a target header and its context as a directed graph to represent their entity types and relations. Then CAST encodes the graph with a relational-aware transformer and uses another transformer to decode the header in the target language. Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models. Our dataset will be released at https://github.com/microsoft/ContextualSP.
%R 10.18653/v1/2021.emnlp-main.5
%U https://aclanthology.org/2021.emnlp-main.5
%U https://doi.org/10.18653/v1/2021.emnlp-main.5
%P 56-66

Markdown (Informal)

[Translating Headers of Tabular Data: A Pilot Study of Schema Translation](https://aclanthology.org/2021.emnlp-main.5) (Zhu et al., EMNLP 2021)

• Translating Headers of Tabular Data: A Pilot Study of Schema Translation (Zhu et al., EMNLP 2021)

ACL

• Kunrui Zhu, Yan Gao, Jiaqi Guo, and Jian-Guang Lou. 2021. Translating Headers of Tabular Data: A Pilot Study of Schema Translation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 56–66, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.